{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:TensorFlow version 2.0.0 detected. Last version known to be fully compatible is 1.14.0 .\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, Flatten, Input\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tfcoreml\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2   3      4      5     6       7   8      9     10  \\\n",
       "0  0.00632  18.0  2.31   0  0.538  6.575  65.2  4.0900   1  296.0  15.3   \n",
       "1  0.02731   0.0  7.07   0  0.469  6.421  78.9  4.9671   2  242.0  17.8   \n",
       "2  0.02729   0.0  7.07   0  0.469  7.185  61.1  4.9671   2  242.0  17.8   \n",
       "3  0.03237   0.0  2.18   0  0.458  6.998  45.8  6.0622   3  222.0  18.7   \n",
       "4  0.06905   0.0  2.18   0  0.458  7.147  54.2  6.0622   3  222.0  18.7   \n",
       "\n",
       "       11    12    13  \n",
       "0  396.90  4.98  24.0  \n",
       "1  396.90  9.14  21.6  \n",
       "2  392.83  4.03  34.7  \n",
       "3  394.63  2.94  33.4  \n",
       "4  396.90  5.33  36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.read_csv(\"../data/housing.csv\", delim_whitespace=True, header=None)\n",
    "dataframe.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "0     506 non-null float64\n",
      "1     506 non-null float64\n",
      "2     506 non-null float64\n",
      "3     506 non-null int64\n",
      "4     506 non-null float64\n",
      "5     506 non-null float64\n",
      "6     506 non-null float64\n",
      "7     506 non-null float64\n",
      "8     506 non-null int64\n",
      "9     506 non-null float64\n",
      "10    506 non-null float64\n",
      "11    506 non-null float64\n",
      "12    506 non-null float64\n",
      "13    506 non-null float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "dataframe.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "5     0\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     0\n",
       "10    0\n",
       "11    0\n",
       "12    0\n",
       "13    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677082</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0           1           2           3           4           5   \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677082   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "               6           7           8           9           10          11  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "               12          13  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((405, 14), (101, 14))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset = dataframe.sample(frac=0.8,random_state=0)\n",
    "test_dataset = dataframe.drop(train_dataset.index)\n",
    "\n",
    "train_dataset.shape, test_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>0</th>\n",
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       "      <td>3.670339</td>\n",
       "      <td>8.977954</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>0.08265</td>\n",
       "      <td>0.26838</td>\n",
       "      <td>3.47428</td>\n",
       "      <td>88.9762</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>405.0</td>\n",
       "      <td>11.067901</td>\n",
       "      <td>22.688396</td>\n",
       "      <td>0.00000</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>12.50000</td>\n",
       "      <td>95.0000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>405.0</td>\n",
       "      <td>11.097951</td>\n",
       "      <td>6.699157</td>\n",
       "      <td>0.74000</td>\n",
       "      <td>5.32000</td>\n",
       "      <td>9.69000</td>\n",
       "      <td>18.10000</td>\n",
       "      <td>27.7400</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>405.0</td>\n",
       "      <td>0.069136</td>\n",
       "      <td>0.253999</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>405.0</td>\n",
       "      <td>0.553098</td>\n",
       "      <td>0.115564</td>\n",
       "      <td>0.38500</td>\n",
       "      <td>0.44900</td>\n",
       "      <td>0.53800</td>\n",
       "      <td>0.62400</td>\n",
       "      <td>0.8710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>405.0</td>\n",
       "      <td>6.269768</td>\n",
       "      <td>0.690552</td>\n",
       "      <td>3.56100</td>\n",
       "      <td>5.87800</td>\n",
       "      <td>6.19300</td>\n",
       "      <td>6.61900</td>\n",
       "      <td>8.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>405.0</td>\n",
       "      <td>68.241975</td>\n",
       "      <td>27.908527</td>\n",
       "      <td>2.90000</td>\n",
       "      <td>45.00000</td>\n",
       "      <td>76.50000</td>\n",
       "      <td>94.10000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>405.0</td>\n",
       "      <td>3.834098</td>\n",
       "      <td>2.162173</td>\n",
       "      <td>1.12960</td>\n",
       "      <td>2.07880</td>\n",
       "      <td>3.27210</td>\n",
       "      <td>5.21460</td>\n",
       "      <td>12.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>405.0</td>\n",
       "      <td>9.474074</td>\n",
       "      <td>8.618564</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>24.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>405.0</td>\n",
       "      <td>405.839506</td>\n",
       "      <td>164.057951</td>\n",
       "      <td>187.00000</td>\n",
       "      <td>284.00000</td>\n",
       "      <td>334.00000</td>\n",
       "      <td>666.00000</td>\n",
       "      <td>711.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>405.0</td>\n",
       "      <td>18.437037</td>\n",
       "      <td>2.141275</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>17.30000</td>\n",
       "      <td>18.80000</td>\n",
       "      <td>20.20000</td>\n",
       "      <td>22.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>405.0</td>\n",
       "      <td>356.276444</td>\n",
       "      <td>91.298597</td>\n",
       "      <td>2.60000</td>\n",
       "      <td>375.21000</td>\n",
       "      <td>391.23000</td>\n",
       "      <td>396.24000</td>\n",
       "      <td>396.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>405.0</td>\n",
       "      <td>12.638914</td>\n",
       "      <td>7.090281</td>\n",
       "      <td>1.73000</td>\n",
       "      <td>7.19000</td>\n",
       "      <td>11.32000</td>\n",
       "      <td>16.65000</td>\n",
       "      <td>37.9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count        mean         std        min        25%        50%        75%  \\\n",
       "0   405.0    3.670339    8.977954    0.00632    0.08265    0.26838    3.47428   \n",
       "1   405.0   11.067901   22.688396    0.00000    0.00000    0.00000   12.50000   \n",
       "2   405.0   11.097951    6.699157    0.74000    5.32000    9.69000   18.10000   \n",
       "3   405.0    0.069136    0.253999    0.00000    0.00000    0.00000    0.00000   \n",
       "4   405.0    0.553098    0.115564    0.38500    0.44900    0.53800    0.62400   \n",
       "5   405.0    6.269768    0.690552    3.56100    5.87800    6.19300    6.61900   \n",
       "6   405.0   68.241975   27.908527    2.90000   45.00000   76.50000   94.10000   \n",
       "7   405.0    3.834098    2.162173    1.12960    2.07880    3.27210    5.21460   \n",
       "8   405.0    9.474074    8.618564    1.00000    4.00000    5.00000   24.00000   \n",
       "9   405.0  405.839506  164.057951  187.00000  284.00000  334.00000  666.00000   \n",
       "10  405.0   18.437037    2.141275   12.60000   17.30000   18.80000   20.20000   \n",
       "11  405.0  356.276444   91.298597    2.60000  375.21000  391.23000  396.24000   \n",
       "12  405.0   12.638914    7.090281    1.73000    7.19000   11.32000   16.65000   \n",
       "\n",
       "         max  \n",
       "0    88.9762  \n",
       "1    95.0000  \n",
       "2    27.7400  \n",
       "3     1.0000  \n",
       "4     0.8710  \n",
       "5     8.7800  \n",
       "6   100.0000  \n",
       "7    12.1265  \n",
       "8    24.0000  \n",
       "9   711.0000  \n",
       "10   22.0000  \n",
       "11  396.9000  \n",
       "12   37.9700  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(13)\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((405, 13), (101, 13), (405,), (101,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels = train_dataset.pop(13)\n",
    "test_labels = test_dataset.pop(13)\n",
    "\n",
    "train_dataset.shape, test_dataset.shape, train_labels.shape, test_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((405, 13), (101, 13), (405,), (101,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def norm(x):\n",
    "  return (x - train_stats['mean']) / train_stats['std']\n",
    "\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)\n",
    "\n",
    "normed_train_data.shape, normed_test_data.shape, train_labels.shape, test_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "normed_train_data.to_csv(\"../data/normed_train_data.csv\", index = None, header=False)\n",
    "normed_test_data.to_csv(\"../data/normed_test_data.csv\", index = None, header=False)\n",
    "train_labels.to_csv(\"../data/train_labels.csv\", index = None, header=False)\n",
    "test_labels.to_csv(\"../data/test_labels.csv\", index = None, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = normed_train_data.join(train_labels)\n",
    "test = normed_test_data.join(test_labels)\n",
    "\n",
    "train.to_csv(\"../data/train.csv\", index = None, header=False)\n",
    "test.to_csv(\"../data/test.csv\", index = None, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = normed_train_data.values\n",
    "Y = train_labels.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras_model = Sequential([\n",
    "    Dense(64, input_shape=[13], activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(1),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.RMSprop(0.001)  #Adam()\n",
    "\n",
    "keras_model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                896       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 3,009\n",
      "Trainable params: 3,009\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "keras_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dense_input\n",
      "dense_2/Identity\n",
      "Identity\n",
      "0 assert nodes deleted\n",
      "['sequential/dense_2/MatMul/ReadVariableOp/resource:0', 'sequential/dense/MatMul/ReadVariableOp/resource:0', 'sequential/dense/BiasAdd/ReadVariableOp/resource:0', 'sequential/dense_2/MatMul/ReadVariableOp:0', 'sequential/dense_2/BiasAdd/ReadVariableOp:0', 'sequential/dense/MatMul/ReadVariableOp:0', 'sequential/dense_1/BiasAdd/ReadVariableOp/resource:0', 'sequential/dense_1/BiasAdd/ReadVariableOp:0', 'sequential/dense_2/BiasAdd/ReadVariableOp/resource:0', 'sequential/dense_1/MatMul/ReadVariableOp:0', 'sequential/dense/BiasAdd/ReadVariableOp:0', 'sequential/dense_1/MatMul/ReadVariableOp/resource:0']\n",
      "6 nodes deleted\n",
      "0 nodes deleted\n",
      "0 nodes deleted\n",
      "[Op Fusion] fuse_bias_add() deleted 6 nodes.\n",
      "2 identity nodes deleted\n",
      "3 disconnected nodes deleted\n",
      "[SSAConverter] Converting function main ...\n",
      "[SSAConverter] [1/6] Converting op type: 'Placeholder', name: 'dense_input', output_shape: (1, 13).\n",
      "[SSAConverter] [2/6] Converting op type: 'MatMul', name: 'sequential/dense/MatMul', output_shape: (1, 64).\n",
      "[SSAConverter] [3/6] Converting op type: 'Relu', name: 'sequential/dense/Relu', output_shape: (1, 64).\n",
      "[SSAConverter] [4/6] Converting op type: 'MatMul', name: 'sequential/dense_1/MatMul', output_shape: (1, 32).\n",
      "[SSAConverter] [5/6] Converting op type: 'Relu', name: 'sequential/dense_1/Relu', output_shape: (1, 32).\n",
      "[SSAConverter] [6/6] Converting op type: 'MatMul', name: 'Identity', output_shape: (1, 1).\n"
     ]
    }
   ],
   "source": [
    "keras_model.save('../model/keras_no_train_model.h5')\n",
    "\n",
    "# get input, output node names for the TF graph from the Keras model\n",
    "input_name = keras_model.inputs[0].name.split(':')[0]\n",
    "keras_output_node_name = keras_model.outputs[0].name.split(':')[0]\n",
    "graph_output_node_name = keras_output_node_name.split('/')[-1]\n",
    "\n",
    "print(input_name)\n",
    "print(keras_output_node_name)\n",
    "print(graph_output_node_name)\n",
    "\n",
    "core_model = tfcoreml.convert(tf_model_path='../model/keras_no_train_model.h5',\n",
    "                         input_name_shape_dict={input_name: (1, 13)},\n",
    "                         output_feature_names=[graph_output_node_name],\n",
    "                         minimum_ios_deployment_target='13')\n",
    "core_model.save('../model/keras_no_train_model.mlmodel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 405 samples\n",
      "Epoch 1/500\n",
      "405/405 [==============================] - 1s 1ms/sample - loss: 558.0246 - mae: 21.9454 - mse: 558.0246\n",
      "Epoch 2/500\n",
      "405/405 [==============================] - 0s 39us/sample - loss: 503.6883 - mae: 20.7222 - mse: 503.6882\n",
      "Epoch 3/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 442.4639 - mae: 19.2979 - mse: 442.4639\n",
      "Epoch 4/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 372.7287 - mae: 17.5371 - mse: 372.7287\n",
      "Epoch 5/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 297.7672 - mae: 15.4228 - mse: 297.7672\n",
      "Epoch 6/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 223.5237 - mae: 12.9528 - mse: 223.5237\n",
      "Epoch 7/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 158.3328 - mae: 10.3922 - mse: 158.3328\n",
      "Epoch 8/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 108.0136 - mae: 8.2130 - mse: 108.0136\n",
      "Epoch 9/500\n",
      "405/405 [==============================] - 0s 38us/sample - loss: 75.7604 - mae: 6.6380 - mse: 75.7604\n",
      "Epoch 10/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 56.7093 - mae: 5.6574 - mse: 56.7093\n",
      "Epoch 11/500\n",
      "405/405 [==============================] - 0s 34us/sample - loss: 45.0442 - mae: 5.0003 - mse: 45.0442\n",
      "Epoch 12/500\n",
      "405/405 [==============================] - 0s 34us/sample - loss: 37.5676 - mae: 4.4999 - mse: 37.5676\n",
      "Epoch 13/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 32.4765 - mae: 4.1270 - mse: 32.4765\n",
      "Epoch 14/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 28.9152 - mae: 3.8410 - mse: 28.9152\n",
      "Epoch 15/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 26.5134 - mae: 3.6356 - mse: 26.5134\n",
      "Epoch 16/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 24.6764 - mae: 3.5167 - mse: 24.6764\n",
      "Epoch 17/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 23.3449 - mae: 3.3620 - mse: 23.3449\n",
      "Epoch 18/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 21.8190 - mae: 3.2611 - mse: 21.8190\n",
      "Epoch 19/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 20.8044 - mae: 3.1396 - mse: 20.8044\n",
      "Epoch 20/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 19.6808 - mae: 3.0422 - mse: 19.6808\n",
      "Epoch 21/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 18.9222 - mae: 3.0028 - mse: 18.9222\n",
      "Epoch 22/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 18.4022 - mae: 2.8758 - mse: 18.4022\n",
      "Epoch 23/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 17.6973 - mae: 2.8660 - mse: 17.6973\n",
      "Epoch 24/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 16.9237 - mae: 2.7924 - mse: 16.9237\n",
      "Epoch 25/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 16.6277 - mae: 2.7625 - mse: 16.6277\n",
      "Epoch 26/500\n",
      "405/405 [==============================] - 0s 40us/sample - loss: 16.0552 - mae: 2.6935 - mse: 16.0552\n",
      "Epoch 27/500\n",
      "405/405 [==============================] - 0s 44us/sample - loss: 15.6601 - mae: 2.6923 - mse: 15.6601\n",
      "Epoch 28/500\n",
      "405/405 [==============================] - 0s 40us/sample - loss: 15.2963 - mae: 2.6576 - mse: 15.2963\n",
      "Epoch 29/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 15.0905 - mae: 2.6155 - mse: 15.0905\n",
      "Epoch 30/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 14.7546 - mae: 2.5768 - mse: 14.7546\n",
      "Epoch 31/500\n",
      "405/405 [==============================] - 0s 38us/sample - loss: 14.4877 - mae: 2.5767 - mse: 14.4877\n",
      "Epoch 32/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 14.1711 - mae: 2.5461 - mse: 14.1711\n",
      "Epoch 33/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 13.9741 - mae: 2.5163 - mse: 13.9741\n",
      "Epoch 34/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 13.7888 - mae: 2.4788 - mse: 13.7888\n",
      "Epoch 35/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 13.4945 - mae: 2.5051 - mse: 13.4945\n",
      "Epoch 36/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 13.4393 - mae: 2.5051 - mse: 13.4393\n",
      "Epoch 37/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 13.1473 - mae: 2.4699 - mse: 13.1473\n",
      "Epoch 38/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 13.0947 - mae: 2.4525 - mse: 13.0947\n",
      "Epoch 39/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 12.8162 - mae: 2.4277 - mse: 12.8162\n",
      "Epoch 40/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 12.9161 - mae: 2.4328 - mse: 12.9161\n",
      "Epoch 41/500\n",
      "405/405 [==============================] - 0s 39us/sample - loss: 12.6877 - mae: 2.4018 - mse: 12.6877\n",
      "Epoch 42/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 12.4212 - mae: 2.3730 - mse: 12.4212\n",
      "Epoch 43/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 12.2197 - mae: 2.3918 - mse: 12.2197\n",
      "Epoch 44/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 12.3429 - mae: 2.3691 - mse: 12.3429\n",
      "Epoch 45/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 12.0076 - mae: 2.3604 - mse: 12.0076\n",
      "Epoch 46/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 12.0044 - mae: 2.3375 - mse: 12.0044\n",
      "Epoch 47/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 11.7579 - mae: 2.3433 - mse: 11.7579\n",
      "Epoch 48/500\n",
      "405/405 [==============================] - 0s 38us/sample - loss: 11.7908 - mae: 2.3278 - mse: 11.7908\n",
      "Epoch 49/500\n",
      "405/405 [==============================] - 0s 37us/sample - loss: 11.8239 - mae: 2.3256 - mse: 11.8239\n",
      "Epoch 50/500\n",
      "405/405 [==============================] - 0s 38us/sample - loss: 11.6103 - mae: 2.3322 - mse: 11.6103\n",
      "Epoch 51/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 11.4834 - mae: 2.3134 - mse: 11.4834\n",
      "Epoch 52/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 11.4897 - mae: 2.2949 - mse: 11.4897\n",
      "Epoch 53/500\n",
      "405/405 [==============================] - 0s 36us/sample - loss: 11.3840 - mae: 2.2866 - mse: 11.3840\n",
      "Epoch 54/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 11.3229 - mae: 2.3094 - mse: 11.3229\n",
      "Epoch 55/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 11.1266 - mae: 2.2661 - mse: 11.1266\n",
      "Epoch 56/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 11.1544 - mae: 2.2531 - mse: 11.1544\n",
      "Epoch 57/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 10.8641 - mae: 2.2666 - mse: 10.8641\n",
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     ]
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     ]
    },
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      "405/405 [==============================] - 0s 34us/sample - loss: 2.0922 - mae: 1.0305 - mse: 2.0922\n",
      "Epoch 496/500\n",
      "405/405 [==============================] - 0s 35us/sample - loss: 2.2240 - mae: 1.0495 - mse: 2.2240\n",
      "Epoch 497/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "405/405 [==============================] - 0s 32us/sample - loss: 2.1084 - mae: 1.0259 - mse: 2.1084\n",
      "Epoch 498/500\n",
      "405/405 [==============================] - 0s 33us/sample - loss: 2.1066 - mae: 1.0260 - mse: 2.1066\n",
      "Epoch 499/500\n",
      "405/405 [==============================] - 0s 33us/sample - loss: 2.1957 - mae: 1.0592 - mse: 2.1957\n",
      "Epoch 500/500\n",
      "405/405 [==============================] - 0s 33us/sample - loss: 2.1238 - mae: 1.0165 - mse: 2.1238\n"
     ]
    }
   ],
   "source": [
    "history = keras_model.fit(X, Y, epochs=500, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = keras_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(405, 1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101/1 - 0s - loss: 10.3712 - mae: 1.9117 - mse: 6.0931\n",
      "Loss :  6.09\n",
      "Mae :  1.91\n",
      "Mse :  6.09\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = keras_model.evaluate(normed_test_data.values, test_labels.values, verbose=2)\n",
    "\n",
    "print(\"Loss : {:5.2f}\".format(loss))\n",
    "print(\"Mae : {:5.2f}\".format(mae))\n",
    "print(\"Mse : {:5.2f}\".format(mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras_model.save('../model/keras_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, 13)\n",
      "dense_input\n",
      "dense_2/Identity\n",
      "Identity\n",
      "0 assert nodes deleted\n",
      "['sequential/dense_2/MatMul/ReadVariableOp/resource:0', 'sequential/dense/MatMul/ReadVariableOp/resource:0', 'sequential/dense/BiasAdd/ReadVariableOp/resource:0', 'sequential/dense_2/MatMul/ReadVariableOp:0', 'sequential/dense_2/BiasAdd/ReadVariableOp:0', 'sequential/dense/MatMul/ReadVariableOp:0', 'sequential/dense_1/BiasAdd/ReadVariableOp/resource:0', 'sequential/dense_1/BiasAdd/ReadVariableOp:0', 'sequential/dense_2/BiasAdd/ReadVariableOp/resource:0', 'sequential/dense_1/MatMul/ReadVariableOp:0', 'sequential/dense/BiasAdd/ReadVariableOp:0', 'sequential/dense_1/MatMul/ReadVariableOp/resource:0']\n",
      "6 nodes deleted\n",
      "0 nodes deleted\n",
      "0 nodes deleted\n",
      "[Op Fusion] fuse_bias_add() deleted 6 nodes.\n",
      "2 identity nodes deleted\n",
      "3 disconnected nodes deleted\n",
      "[SSAConverter] Converting function main ...\n",
      "[SSAConverter] [1/6] Converting op type: 'Placeholder', name: 'dense_input', output_shape: (1, 13).\n",
      "[SSAConverter] [2/6] Converting op type: 'MatMul', name: 'sequential/dense/MatMul', output_shape: (1, 64).\n",
      "[SSAConverter] [3/6] Converting op type: 'Relu', name: 'sequential/dense/Relu', output_shape: (1, 64).\n",
      "[SSAConverter] [4/6] Converting op type: 'MatMul', name: 'sequential/dense_1/MatMul', output_shape: (1, 32).\n",
      "[SSAConverter] [5/6] Converting op type: 'Relu', name: 'sequential/dense_1/Relu', output_shape: (1, 32).\n",
      "[SSAConverter] [6/6] Converting op type: 'MatMul', name: 'Identity', output_shape: (1, 1).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "input {\n",
       "  name: \"dense_input\"\n",
       "  type {\n",
       "    multiArrayType {\n",
       "      shape: 1\n",
       "      shape: 13\n",
       "      dataType: DOUBLE\n",
       "    }\n",
       "  }\n",
       "}\n",
       "output {\n",
       "  name: \"Identity\"\n",
       "  type {\n",
       "    multiArrayType {\n",
       "      dataType: DOUBLE\n",
       "    }\n",
       "  }\n",
       "}\n",
       "metadata {\n",
       "  userDefined {\n",
       "    key: \"coremltoolsVersion\"\n",
       "    value: \"3.1\"\n",
       "  }\n",
       "}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(keras_model.input_shape)\n",
    "\n",
    "# get input, output node names for the TF graph from the Keras model\n",
    "input_name = keras_model.inputs[0].name.split(':')[0]\n",
    "keras_output_node_name = keras_model.outputs[0].name.split(':')[0]\n",
    "graph_output_node_name = keras_output_node_name.split('/')[-1]\n",
    "\n",
    "print(input_name)\n",
    "print(keras_output_node_name)\n",
    "print(graph_output_node_name)\n",
    "\n",
    "core_model = tfcoreml.convert(tf_model_path='../model/keras_model.h5',\n",
    "                         input_name_shape_dict={input_name: (1, 13)},\n",
    "                         output_feature_names=[graph_output_node_name],\n",
    "                         minimum_ios_deployment_target='13')\n",
    "core_model.save('../model/keras_model.mlmodel')\n",
    "\n",
    "core_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "specificationVersion: 4\n",
       "description {\n",
       "  input {\n",
       "    name: \"dense_input\"\n",
       "    type {\n",
       "      multiArrayType {\n",
       "        shape: 1\n",
       "        shape: 13\n",
       "        dataType: DOUBLE\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  output {\n",
       "    name: \"Identity\"\n",
       "    type {\n",
       "      multiArrayType {\n",
       "        dataType: DOUBLE\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  metadata {\n",
       "    userDefined {\n",
       "      key: \"coremltoolsVersion\"\n",
       "      value: \"3.1\"\n",
       "    }\n",
       "  }\n",
       "}\n",
       "neuralNetwork {\n",
       "  layers {\n",
       "    name: \"sequential/dense/MatMul\"\n",
       "    input: \"dense_input\"\n",
       "    output: \"sequential/dense/MatMul\"\n",
       "    inputTensor {\n",
       "      rank: 2\n",
       "      dimValue: 1\n",
       "      dimValue: 13\n",
       "    }\n",
       "    outputTensor {\n",
       "      rank: 2\n",
       "      dimValue: 1\n",
       "      dimValue: 64\n",
       "    }\n",
       "    batchedMatmul {\n",
       "      weightMatrixFirstDimension: 13\n",
       "      weightMatrixSecondDimension: 64\n",
       "      hasBias: true\n",
       "      weights {\n",
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       "        floatValue: 0.40020668506622314\n",
       "        floatValue: 0.32317081093788147\n",
       "        floatValue: 0.46019554138183594\n",
       "        floatValue: 0.2263045459985733\n",
       "        floatValue: 0.6153852939605713\n",
       "        floatValue: 0.2873143255710602\n",
       "        floatValue: -0.9814534187316895\n",
       "        floatValue: 0.555921733379364\n",
       "        floatValue: -0.15192627906799316\n",
       "        floatValue: 0.5098896026611328\n",
       "        floatValue: 0.6206872463226318\n",
       "        floatValue: 0.438157320022583\n",
       "        floatValue: 0.35508987307548523\n",
       "        floatValue: 0.18825244903564453\n",
       "        floatValue: -0.3407880365848541\n",
       "        floatValue: -1.0855002403259277\n",
       "        floatValue: -0.39441978931427\n",
       "        floatValue: -1.7252968549728394\n",
       "        floatValue: -1.4871857166290283\n",
       "        floatValue: -1.5946946144104004\n",
       "        floatValue: 0.24364720284938812\n",
       "        floatValue: 0.4844973683357239\n",
       "        floatValue: -1.769595742225647\n",
       "        floatValue: 0.5340834856033325\n",
       "        floatValue: 0.42973095178604126\n",
       "      }\n",
       "      bias {\n",
       "        floatValue: 0.15765133500099182\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  arrayInputShapeMapping: EXACT_ARRAY_MAPPING\n",
       "  imageInputShapeMapping: RANK4_IMAGE_MAPPING\n",
       "}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec = core_model.get_spec()\n",
    "spec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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